Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China.
Department of Orthopedics, Hainan Hospital of Chinese PLA General Hospital, Hainan, China.
J Med Internet Res. 2023 Oct 23;25:e47590. doi: 10.2196/47590.
Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions.
This study aimed to develop a machine learning-based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis.
This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches-logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine-were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model.
In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was -0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator.
This study develops a machine learning-based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.
患有骨转移的患者通常生存时间明显受限，一般预期寿命<3 个月被认为是广泛侵袭性手术的禁忌症。在这种情况下，准确预测生存情况变得非常重要，因为它是做出临床决策的关键指南。
本研究旨在开发一种基于机器学习的网络计算器，能够对骨转移患者的早期死亡可能性进行准确评估。
本研究使用从国家癌症数据库获得的数据，对 2010 年至 2019 年间诊断为骨转移的 118227 名患者进行了一项大型队列研究。将所有患者随机分为 9:1 的训练组（n=106492）和验证组（n=11735）。本研究实施了六种方法——逻辑回归、极端梯度提升机、决策树、随机森林、神经网络和梯度提升机。使用 11 个指标评估这些方法的性能，并根据每个指标的性能对每个方法进行排名。使用教学医院的 332 名患者作为外部验证组，并使用最优模型进行外部验证。
在整个队列中，相当一部分患者（43305/118227，36.63%）发生早期死亡。在所评估的不同方法中，梯度提升机表现出最高的预测性能评分（54 分），其次是神经网络（52 分）和极端梯度提升机（50 分）。梯度提升机表现出良好的判别能力，曲线下面积为 0.858（95%CI 0.851-0.865）。此外，校准斜率为 1.02，截距较大值为-0.02，表明模型校准良好。使用梯度提升机，根据 37%的阈值将患者分为 2 个风险组。高风险组（3105/4315，71.96%）的患者发生早期死亡的可能性是低风险组（1159/7420，15.62%）的 4.5 倍。模型的外部验证显示出较高的曲线下面积为 0.847（95%CI 0.798-0.895），表明其性能稳健。基于梯度提升机的模型已在互联网上作为计算器部署。
本研究开发了一种基于机器学习的计算器，用于评估骨转移患者早期死亡的概率。该计算器有可能通过识别那些有更高早期死亡风险的患者，为临床决策提供指导，改善骨转移患者的护理。
